perbase 1.4.0

Fast and correct perbase BAM/CRAM analysis.
Documentation 
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#!/usr/bin/env python3
"""
Generate benchmark plots for the JOSS paper - simplified version
"""

import pandas as pd
import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

# Load data
data_dir = Path("data/outputs")
base_depth_df = pd.read_csv(data_dir / "bench_results_base_depth.csv")
mate_fix_df = pd.read_csv(data_dir / "bench_results_base_depth_mate_fix.csv")

# Extract tool names
base_depth_df["tool"] = base_depth_df["command"].apply(lambda x: x.split()[0])
mate_fix_df["tool"] = mate_fix_df["command"].apply(lambda x: x.split()[0])

# Create output directory
output_dir = Path("outputs")
output_dir.mkdir(exist_ok=True)

# Create a simple comparison plot
fig, ax = plt.subplots(1, 1, figsize=(8, 6))

# Prepare data
tools = ["perbase", "sambamba"]
modes = ["Standard", "Mate-fix"]

perbase_std = base_depth_df[base_depth_df["tool"] == "perbase"]["mean"].values[0] / 60  # Convert to minutes
sambamba_std = base_depth_df[base_depth_df["tool"] == "sambamba"]["mean"].values[0] / 60
perbase_mf = mate_fix_df[mate_fix_df["tool"] == "perbase"]["mean"].values[0] / 60
sambamba_mf = mate_fix_df[mate_fix_df["tool"] == "sambamba"]["mean"].values[0] / 60

# Group by mode instead of tool
x = np.arange(len(modes))
width = 0.35

# perbase bars
bars1 = ax.bar(
    x - width / 2, [perbase_std, perbase_mf], width, label="perbase", color="#1f77b4"
)
# sambamba bars
bars2 = ax.bar(
    x + width / 2, [sambamba_std, sambamba_mf], width, label="sambamba", color="#ff7f0e"
)

ax.set_ylabel("Runtime (minutes)")
ax.set_xlabel("Mode")
ax.set_title("perbase vs sambamba: base-depth Performance")
ax.set_xticks(x)
ax.set_xticklabels(modes)
ax.legend()

# Calculate speedup for print output
speedup_std = sambamba_std / perbase_std
speedup_mf = sambamba_mf / perbase_mf

# Save plot
plt.savefig(output_dir / "benchmark_comparison.png", dpi=300, bbox_inches="tight")
plt.savefig(output_dir / "benchmark_comparison.pdf", bbox_inches="tight")

print("Plot generated successfully!")
print(f"Standard mode: perbase is {speedup_std:.1f}x faster than sambamba")
print(f"Mate-fix mode: perbase is {speedup_mf:.1f}x faster than sambamba")